# tdw
model <- fb %>%
lm(TDW ~ block + gnt*treat, data = .)
anova(model)
## Analysis of Variance Table
##
## Response: TDW
## Df Sum Sq Mean Sq F value Pr(>F)
## block 4 1797.3 449.3 5.0193 0.0009244 ***
## gnt 14 20614.5 1472.5 16.4490 < 0.00000000000000022 ***
## treat 1 9345.0 9345.0 104.3939 < 0.00000000000000022 ***
## gnt:treat 14 2126.7 151.9 1.6969 0.0657444 .
## Residuals 113 10115.4 89.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ treat | gnt) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws))
mcTDW <- emmeans(model, ~ gnt | treat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
mutate(across(emmean, ~round(., 2))) %>%
unite(TDW, c(emmean, .group), sep = " ") %>%
dplyr::select(1:3)
tdw <- mc %>%
plot_smr(type = "line"
, color = F
, x = "gnt"
, xlab = ""
, y = "emmean"
, ylab = "Tuber dry weight (g)"
, group = "treat"
, glab = "Treatment"
, legend = "none"
, error = "SE"
, ylimits = c(0, 100, 20)
, sig = ".group"
)
# rcc
model <- fb %>%
lm(RCC ~ block + gnt*treat, data = .)
anova(model)
## Analysis of Variance Table
##
## Response: RCC
## Df Sum Sq Mean Sq F value Pr(>F)
## block 4 0.0001547 0.0000387 0.9411 0.443576
## gnt 14 0.0126889 0.0009063 22.0533 < 0.00000000000000022 ***
## treat 1 0.0059808 0.0059808 145.5241 < 0.00000000000000022 ***
## gnt:treat 14 0.0013211 0.0000944 2.2961 0.009104 **
## Residuals 98 0.0040276 0.0000411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ treat | gnt) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws))
mcRCC <- emmeans(model, ~ gnt | treat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
mutate(across(emmean, ~round(., 2))) %>%
unite(RCC, c(emmean, .group), sep = " ") %>%
dplyr::select(1:3)
rcc <- mc %>%
plot_smr(type = "line"
, color = F
, x = "gnt"
, xlab = ""
, y = "emmean"
, ylab = "Relative chlorophyll content"
, ylimits = c(0, 0.1, 0.02)
, group = "treat"
, glab = "Treatment"
, legend = "none"
, error = "SE"
, sig = ".group"
)
# hi
model <- fb %>%
lm(HI ~ block + gnt*treat, data = .)
anova(model)
## Analysis of Variance Table
##
## Response: HI
## Df Sum Sq Mean Sq F value Pr(>F)
## block 4 0.11840 0.029599 8.9657 0.00000251877 ***
## gnt 14 2.65590 0.189707 57.4634 < 0.00000000000000022 ***
## treat 1 0.12163 0.121629 36.8421 0.00000001753 ***
## gnt:treat 14 0.07233 0.005167 1.5650 0.1001
## Residuals 113 0.37305 0.003301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ treat | gnt) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws))
mcHI <- emmeans(model, ~ gnt | treat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
mutate(across(emmean, ~round(., 2))) %>%
unite(HI, c(emmean, .group), sep = " ") %>%
dplyr::select(1:3)
hi <- mc %>%
plot_smr(type = "line"
, color = F
, x = "gnt"
, xlab = ""
, y = "emmean"
, ylab = "Harvest Index"
, group = "treat"
, glab = "Treatment"
, legend = "none"
, ylimits = c(0, 1, 0.2)
, error = "SE"
, sig = ".group"
)
# wue_t
model <- fb %>%
lm(WUE_T ~ block + gnt*treat, data = .)
anova(model)
## Analysis of Variance Table
##
## Response: WUE_T
## Df Sum Sq Mean Sq F value Pr(>F)
## block 4 28.55 7.1382 10.4548 0.0000003055 ***
## gnt 14 400.34 28.5959 41.8820 < 0.00000000000000022 ***
## treat 1 2.02 2.0164 2.9533 0.08844 .
## gnt:treat 14 14.80 1.0572 1.5485 0.10534
## Residuals 113 77.15 0.6828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ treat | gnt) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws))
mcWUE_T <- emmeans(model, ~ gnt | treat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
mutate(across(emmean, ~round(., 2))) %>%
unite(WUE_T, c(emmean, .group), sep = " ") %>%
dplyr::select(1:3)
wue_t <- mc %>%
plot_smr(type = "line", color = F
, x = "gnt"
, xlab = ""
, y = "emmean"
, ylab = "Tuber water use efficiency (g/L)"
, group = "treat"
, glab = "Treatment"
, legend = "none"
, ylimits = c(0, 10, 2)
, error = "SE"
, sig = ".group"
)
# spad_29
model <- fb %>%
lm(SPAD_29 ~ block + gnt*treat, data = .)
anova(model)
## Analysis of Variance Table
##
## Response: SPAD_29
## Df Sum Sq Mean Sq F value Pr(>F)
## block 4 26.27 6.567 0.7227 0.5781
## gnt 14 2396.29 171.163 18.8364 <0.0000000000000002 ***
## treat 1 12.87 12.866 1.4159 0.2365
## gnt:treat 14 146.78 10.484 1.1538 0.3202
## Residuals 116 1054.07 9.087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ treat | gnt) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws))
mcSPAD_29 <- emmeans(model, ~ gnt | treat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
mutate(across(emmean, ~round(., 2))) %>%
unite(SPAD_29, c(emmean, .group), sep = " ") %>%
dplyr::select(1:3)
spad_29 <- mc %>%
mutate(treat = case_when(
treat == "WD" ~ "Water Deficit (WD)"
, treat == "WW" ~ "Well Watered (WW)"
)) %>%
plot_smr(type = "line"
, x = "gnt"
, xlab = ""
, y = "emmean"
, ylab = "SPAD at 29 dap"
, group = "treat"
, glab = "Treatment"
, legend = "top"
, ylimits = c(30, 70, 5)
, error = "SE"
, color = F
, sig = ".group"
)
# spad_83
model <- fb %>%
lm(SPAD_83 ~ block + gnt*treat, data = .)
anova(model)
## Analysis of Variance Table
##
## Response: SPAD_83
## Df Sum Sq Mean Sq F value Pr(>F)
## block 4 87.52 21.88 1.5528 0.19163
## gnt 14 1951.83 139.42 9.8947 0.00000000000003419 ***
## treat 1 722.05 722.05 51.2451 0.00000000007974715 ***
## gnt:treat 14 436.61 31.19 2.2134 0.01098 *
## Residuals 116 1634.44 14.09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ treat | gnt) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws))
mcSPAD_83 <- emmeans(model, ~ gnt | treat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
mutate(across(emmean, ~round(., 2))) %>%
unite(SPAD_83, c(emmean, .group), sep = " ") %>%
dplyr::select(1:3)
spad_83 <- mc %>%
plot_smr(type = "line", color = F
, x = "gnt"
, xlab = ""
, y = "emmean"
, ylab = "SPAD at 83 dap"
, group = "treat"
, glab = "Treatment"
, legend= "none"
, ylimits = c(30, 70, 5)
, error = "SE"
, sig = ".group"
)
plot <- ggdraw(xlim = c(0.0, 1), ylim = c(0.0, 1)) +
draw_plot(spad_29 , width = 0.49, height = 0.35, x = 0.0, y = 0.6) +
draw_plot(spad_83, width = 0.49, height = 0.3, x = 0.5, y = 0.6) +
draw_plot(rcc, width = 0.49, height = 0.3, x = 0.0, y = 0.3) +
draw_plot(tdw, width = 0.49, height = 0.3, x = 0.5, y = 0.3) +
draw_plot(hi , width = 0.49, height = 0.3, x = 0.0, y = 0.0) +
draw_plot(wue_t, width = 0.49, height = 0.3, x = 0.5, y = 0.0) +
draw_plot_label(
label = c("a", "b", "c", "d", "e", "f"),
x = c(0.06, 0.56, 0.06, 0.56, 0.06, 0.56),
y = c(0.89, 0.89, 0.59, 0.59, 0.29, 0.29))
plot %>%
ggsave(plot = .
, "files/Fig2.pdf"
, units = "cm", width = 28, height = 25)
"files/Fig2.pdf" %>% include_graphics()
